DEMANDS ESTIMATION OF NEW TELECOMMUNICATION SERVICES IN FUZZY ENVIRONMENT

2003 ◽  
Vol 02 (02) ◽  
pp. 333-347 ◽  
Author(s):  
LONG-SHUH LIN
Keyword(s):  
Historical Data ◽  
Estimation Method ◽  
Solution Procedure ◽  
Influential Factors ◽  
Estimation Model ◽  
Telecommunication Services ◽  
Fuzzy Environment ◽  
Proposed Model ◽  
Phone Service ◽  
One Year

With the uncertain influential factors of demands and the lack of required historical data, demand estimation for new telecommunication services have generally relied just on marketing survey and analysis. However, the data collected from marketing survey are usually expressed in human linguistic forms and hence are fuzzy in nature. That means the estimation method derived from traditional sampling theory cannot fully represent such fuzzy data and thus biased consequences caused often. Therefore, in this study, to completely capture the uncertainty of the surveyed data, we adopt a series of analytical methods based on fuzzy set theory to construct a fuzzy estimation model. Based on the proposed model, a solution procedure is developed to aid users to acquire the demands of new telecommunication services. Finally, the solution procedure is employed to estimate demands of mobile phone service within one year in Taiwan with satisfactory results.

scholarly journals Research on the prediction of quay crane resource hour based on Ensemble Learning

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Gaoshen Wang ◽  
Yi Ding
Keyword(s):  
Ensemble Learning ◽  
Historical Data ◽  
Influence Factors ◽  
Estimation Method ◽  
Wait Time ◽  
Container Terminals ◽  
Estimation Model ◽  
Empirical Estimation ◽  
Preparation Time ◽  
Adaboost Algorithm

In Container terminals, a quay crane's resource hour is affected by various complex nonlinear factors, and it is not easy to make a forecast quickly and accurately. Most ports adopt the empirical estimation method at present, and most of the studies assumed that accurate quay crane’s resource hour could be obtained in advance. Through the ensemble learning (EL) method, the influence factors and correlation of quay crane’s resources hour were analyzed based on a large amount of historical data. A multi-factor ensemble learning estimation model based quay crane’s resource hour was established. Through a numerical example, it is finally found that Adaboost algorithm has the best effect of prediction, with an error of 1.5%. Through the example analysis, it comes to a conclusion: the error is 131.86% estimated by the experience method. It will lead that subsequent shipping cannot be serviced as scheduled, increasing the equipment wait time and preparation time, and generating additional cost and energy consumption. In contrast, the error based Adaboost learning estimation method is 12.72%. So Adaboost has better performance.

scholarly journals Assessment of Electric Vehicle Charging Costs in Presence of Distributed Photovoltaic Generation and Variable Electricity Tariffs

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 499 ◽  
Author(s):  
Marco Pasetti ◽  
Stefano Rinaldi ◽  
Alessandra Flammini ◽  
Michela Longo ◽  
Federica Foiadelli
Keyword(s):  
Cost Estimation ◽  
Energy Savings ◽  
Estimation Method ◽  
Pv System ◽  
Reference Case ◽  
Electric Vehicle Charging ◽  
Proposed Model ◽  
Supply Equipment ◽  
One Year ◽  
Ev Charging

In this paper a general model for the estimation of the uncoordinated charging costs of Electric Vehicles (EVs) in the presence of distributed and intermittent generation, and variable electricity tariffs is presented. The proposed method aims at estimating the monthly average cost of uncoordinated charging of a single EV depending on the hour at which the EV is plugged into the EV Supply Equipment (EVSE). The feasibility and relevance of the proposed model is verified by applying the considered cost estimation method to a suitable use case. A single EV charging service offered at a public building equipped with a Photovoltaic (PV) system has been considered as reference case. The proposed model has been applied to the PV production and loads consumption data collected during one year, and the results of the study compared with the Time-Of-Use (TOU) electricity tariff. The application of the proposed model identified noticeable deviations among the computed EV charging costs and the reference TOU profile, with differences up to 40%, depending on the considered month and on the time of charging during the day. It can be concluded that such model could be used to properly detect opportunities of energy savings, and to define dedicated EV price signals that could help to promote the optimal use of distributed energy resources.

Demand Forecasting Model of Port Critical Spare Parts Based on PSO-LSSVM

2013 ◽  
Vol 433-435 ◽  
pp. 545-549
Author(s):  
Zhi Jie Song ◽  
Zan Fu ◽  
Han Wang ◽  
Gui Bin Hou
Keyword(s):  
Historical Data ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Influential Factors ◽  
Support Vector ◽  
Proposed Model ◽  
Svm Model ◽  
Vector Machines ◽  
Demand Forecasting Model

Demand forecasting for port critical spare parts (CSP) is notoriously difficult as it is expensive, lumpy and intermittent with high variability. In this paper, some influential factors which have an effect on CSP consumption were proposed according to port CSP characteristics and historical data. Combined with the influential factors, a least squares support vector machines (LS-SVM) model optimized by particle swarm optimization (PSO) was developed to forecast the demand. And the effectiveness of the model is demonstrated through a real case study, which shows that the proposed model can forecast the demand of port CSP more accurately, and effectively reduce inventory backlog.

scholarly journals Cost Estimation Model Using Modified Parameters for Construction Projects

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Sae-Hyun Ji ◽  
Joseph Ahn ◽  
Hyun-Soo Lee ◽  
Kyeongjin Han
Keyword(s):  
Cost Estimation ◽  
Construction Projects ◽  
Cost Model ◽  
Influence Factors ◽  
Influential Factors ◽  
Estimation Accuracy ◽  
Estimation Model ◽  
Proposed Model ◽  
Cost Estimation Model ◽  
The Cost

Construction projects require huge amounts of capital and have many risk factors due to the unique industry characteristics. For a project to be successful, accurate cost estimation during the design phase is very important. Thus, this research aims to develop a cost estimation model where a modification method integrates influential factors with significant parameters. This study identified a modified parameter-making process, which integrates many influential factors into a small number of significant parameters. The proposed model estimates the cost using quantity-based modified parameters multiplied by their price. A case study was conducted with 24-residence building project, and the estimation accuracy of the suggested method and a CBR model were compared. The proposed model achieved higher overall cost-estimation accuracy and stability. A large number of influence factors can be modified as simple representatives and overcome the limitations of a conventional cost estimation model. The paper originality relates to providing a modified parameter-making process to enhance reliability of a cost estimation. In addition, the suggested cost model can actively respond to the iterative requirements of recalculation of the cost.

Normalized Sprint Estimation

2019 ◽  
Vol 7 (2) ◽  
pp. 41-58
Author(s):  
Shailesh Kumar
Keyword(s):  
Dynamic Characteristics ◽  
Success Factors ◽  
Historical Data ◽  
Estimation Method ◽  
Accurate Estimation ◽  
Functional Requirements ◽  
Estimation Model ◽  
Software Projects ◽  
Project Estimation

Accurate estimation of software projects is quintessential for overall success of the project. Estimation of agile projects adopted in most of the modern software projects is challenging due to lack of historical data and due to dynamic characteristics of the agile projects. In this paper we introduce “Normalized Sprint Estimation” method which factors in dynamic characteristics of the agile projects such as non-functional requirements, sprint success factors and such. The author applied the normalized sprint estimation method to 14 sprints from three digital projects and the predicted estimation values had Pred (0.3), more than 80%. Though the normalized sprint estimation model is tested for digital projects, the same methodology can be applied for software projects from other domains as well.

Estimation of Forest Biomass Energy Resources Based on Bottom-up Approach in China

2015 ◽  
Vol 8 (1) ◽  
pp. 272-275
Author(s):  
Lan Zhang ◽  
Dan Yu ◽  
Caihong Zhang ◽  
Weidong Zhang
Keyword(s):  
Forest Biomass ◽  
Estimation Method ◽  
Energy Resource ◽  
Biomass Energy ◽  
Energy Resources ◽  
Theoretical Ground ◽  
Estimation Model ◽  
Bottom Up ◽  
Rational Development ◽  
Calculation Results

Currently, the forest biomass energy development is at an initial stage and the estimation method for the forest biomass energy resource reserve is to be unified and refined although there is a great value and potential in the development and utilization of forest biomass energy in China. Based on the existing studies, the present paper analyzes the origins and types of forest biomass energy resources in the perspective of sustainable forestry management, constructs the estimation model using a bottom-up approach, and estimates the total existing forest biomass energy resource reserve in China based on the data of the 7th Forest Resource Survey. The estimation method and the calculation results provide the important theoretical ground for promoting the rational development of forest biomass energy in China.

scholarly journals Estimation of resistance force at steady-state sinkage for cylindrical wheel-typed lunar/planetary exploration rovers with function of push–pull locomotion

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Daisuke Fujiwara ◽  
Naoki Tsujikawa ◽  
Tetsuya Oshima ◽  
Kojiro Iizuka
Keyword(s):  
Steady State ◽  
Estimation Method ◽  
Planetary Exploration ◽  
Experimental Result ◽  
Resistance Force ◽  
Estimation Model ◽  
Loose Soil ◽  
Key Factor ◽  
The Relationship ◽  
Resistance Forces

Abstract Planetary exploration rovers have required a high traveling performance to overcome obstacles such as loose soil and rocks. Push-pull locomotion rovers is a unique scheme, like an inchworm, and it has high traveling performance on loose soil. Push-pull locomotion uses the resistance force by keeping a locked-wheel related to the ground, whereas the conventional rotational traveling uses the shear force from loose soil. The locked-wheel is a key factor for traveling in the push-pull scheme. Understanding the sinking behavior and its resistance force is useful information for estimating the rover’s performance. Previous studies have reported the soil motion under the locked-wheel, the traction, and the traveling behavior of the rover. These studies were, however, limited to the investigation of the resistance force and amount of sinkage for the particular condition depending on the rover. Additionally, the locked-wheel sinks into the soil until it obtains the required force for supporting the other wheels’ motion. How the amount of sinkage and resistance forces are generated at different wheel sizes and mass of an individual wheel has remained unclear, and its estimation method hasn’t existed. This study, therefore, addresses the relationship between the sinkage and its resistance force, and we analyze and consider this relationship via the towing experiment and theoretical consideration. The results revealed that the sinkage reached a steady-state value and depended on the contact area and mass of each wheel, and the maximum resistance force also depends on this sinkage. Additionally, the estimation model did not capture the same trend as the experimental results when the wheel width changed, whereas, the model captured a relatively the same trend as the experimental result when the wheel mass and diameter changed.

Designing a sustainable supply chain network for producing high-value products from waste glass

2021 ◽  
pp. 0734242X2199466
Author(s):  
Naeme Zarrinpoor
Keyword(s):  
Supply Chain ◽  
Greenhouse Gas Emission ◽  
Fuzzy Programming ◽  
Solution Procedure ◽  
Waste Glass ◽  
Worker Safety ◽  
Programming Approach ◽  
Supply Chain Network ◽  
Sustainable Supply Chain ◽  
Proposed Model

This paper aims to design a supply chain network for producing double glazed glass from the recycling of waste glass. All three pillars of sustainability are taken into consideration. The economic objective tries to maximize total profits. The environmental objective considers the energy consumption, the generated waste, the greenhouse gas emission, the water consumption, and the fuel consumption of vehicles. The social objective addresses created job opportunities, the worker safety, the regional development, the worker benefit, and training hours. To solve the model, a two-stage framework based on the group best-worst method and an interactive fuzzy programming approach is developed. The proposed model is validated through a real case study based on waste glass management in the city of Shiraz. It is revealed that when sustainable development goals are approached, a great degree of improvement will be attained in environmental and social aspects without a significant decrease in the economic sustainability. The results also demonstrate that the locations of glass recycling centres are different under economic, environmental, and social pillars, and the proposed model yields an optimal system configuration with a proper satisfaction degree of all objectives. Moreover, applying the proposed solution procedure enables system designers to obtain the most desirable trade-off between different aspects of sustainability.

scholarly journals Inertial Sensor-Based Step Length Estimation Model by Means of Principal Component Analysis

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3527
Author(s):  
Melanija Vezočnik ◽  
Roman Kamnik ◽  
Matjaz B. Juric
Keyword(s):  
Principal Component Analysis ◽  
Human Body ◽  
Inertial Sensor ◽  
Principal Component ◽  
Step Length ◽  
Component Analysis ◽  
Indoor Positioning ◽  
Estimation Model ◽  
Length Estimation ◽  
Proposed Model

Inertial sensor-based step length estimation has become increasingly important with the emergence of pedestrian-dead-reckoning-based (PDR-based) indoor positioning. So far, many refined step length estimation models have been proposed to overcome the inaccuracy in estimating distance walked. Both the kinematics associated with the human body during walking and actual step lengths are rarely used in their derivation. Our paper presents a new step length estimation model that utilizes acceleration magnitude. To the best of our knowledge, we are the first to employ principal component analysis (PCA) to characterize the experimental data for the derivation of the model. These data were collected from anatomical landmarks on the human body during walking using a highly accurate optical measurement system. We evaluated the performance of the proposed model for four typical smartphone positions for long-term human walking and obtained promising results: the proposed model outperformed all acceleration-based models selected for the comparison producing an overall mean absolute stride length estimation error of 6.44 cm. The proposed model was also least affected by walking speed and smartphone position among acceleration-based models and is unaffected by smartphone orientation. Therefore, the proposed model can be used in the PDR-based indoor positioning with an important advantage that no special care regarding orientation is needed in attaching the smartphone to a particular body segment. All the sensory data acquired by smartphones that we utilized for evaluation are publicly available and include more than 10 h of walking measurements.

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

2019 ◽  
Vol 2673 (4) ◽  
pp. 108-116 ◽  
Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Safety Management ◽  
Estimation Method ◽  
Estimation Model ◽  
The Neural Network ◽  
Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

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